Zhengzheng Xiang

Robust Beamforming for Wireless Information and Power Transmission

In this letter, we study the robust beamforming problem for the multi-antenna wireless broadcasting system with simultaneous information and power transmission, under the assumption of imperfect channel state information (CSI) at the transmitter. Following the worst-case deterministic model, our objective is to maximize the worst-case harvested energy for the energy receiver while guaranteeing that the rate for the information receiver is above a threshold for all possible channel realizations. Such problem is nonconvex with infinite number of constraints. Using certain transformation techniques, we convert this problem into a relaxed semidefinite programming problem (SDP) which can be solved efficiently. We further show that the solution of the relaxed SDP problem is always rank-one. This indicates that the relaxation is tight and we can get the optimal solution for the original problem. Simulation results are presented to validate the effectiveness of the proposed algorithm.

Coordinated Multicast Beamforming in Multicell Networks

We study physical layer multicasting in multicell networks where each base station, equipped with multiple antennas, transmits a common message using a single beamformer to multiple users in the same cell. We investigate two coordinated beamforming designs: the quality-of-service (QoS) beamforming and the max-min SINR (signal-to-interference-plus-noise ratio) beamforming. The goal of the QoS beamforming is to minimize the total power consumption while guaranteeing that received SINR at each user is above a predetermined threshold. We present a necessary condition for the optimization problem to be feasible. Then, based on the decomposition theory, we propose a novel decentralized algorithm to implement the coordinated beamforming with limited information sharing among different base stations. The algorithm is guaranteed to converge and in most cases it converges to the optimal solution. The max-min SINR (MMS) beamforming is to maximize the minimum received SINR among all users under per-base station power constraints. We show that the MMS problem and a weighted peak-power minimization (WPPM) problem are inverse problems. Based on this inversion relationship, we then propose an efficient algorithm to solve the MMS problem in an approximate manner. Simulation results demonstrate significant advantages of the proposed multicast beamforming algorithms over conventional multicasting schemes.

Massive MIMO Multicasting in Noncooperative Cellular Networks

We study physical layer multicasting in cellular networks where each base station (BS) is equipped with a very large number of antennas and transmits a common message using a single beamformer to multiple mobile users. The messages sent by different BSs are independent, and the BSs do not cooperate. We first show that when each BS knows the perfect channel state information (CSI) of its own served users, the asymptotically optimal beamformer at each BS is a linear combination of the channel vectors of its multicast users. Moreover, the optimal and explicit combining coefficients are obtained. Then we consider the imperfect CSI scenario where the CSI is obtained through uplink channel estimation in time-division duplex systems. We propose a new pilot scheme that estimates the composite channel, which is a linear combination of the individual channels of multicast users in each cell. This scheme is able to completely eliminate pilot contamination. The pilot power control for optimizing the multicast beamformer at each BS is also derived. Numerical results show that the asymptotic performance of the proposed scheme is close to the ideal case with perfect CSI. Simulation also verifies the effectiveness of the proposed scheme with finite number of antennas at each BS.

Massive MIMO multicasting in noncooperative multicell networks

We study the massive MIMO (multiple-input multiple-output) multicast transmission in multicell networks, where each base station (BS) is equipped with a large-scale antenna array and transmits a common message using a single beamformer to multiple mobile users. We first consider the case when each BS knows the perfect channel state information (CSI) of all its served users. We show that the asymptotically optimal beamformer structure at each BS is a linear combination of the channel vectors of its multicast users. The optimal combination coefficients are also obtained in closed form. Then we consider the imperfect CSI scenario where each BS obtains the CSI through uplink channel estimation. We propose a novel pilot scheme that estimates the compound channel rather than the individual channels of multicast users in each cell. This scheme is able to completely eliminate pilot contamination. The optimal power control of pilot transmission is also derived. Numerical results show that the performance of the proposed pilot scheme with pilot power control is close to that of the perfect CSI case.

On Degrees of Freedom of Cognitive Networks with User Cooperation

In this letter, the degrees of freedom (DoF) outer bound is characterized for cognitive networks. We have devised an interference alignment scheme with appropriate cooperation between primary users and secondary users to achieve the DoF outer bound. Moreover, to encourage the primary users to participate in the proposed cooperation, we also design two aided interference alignment schemes, where both the primary users and the secondary users can tap more DoF out of the cognitive channels than the existing systems and thereby benefit from the cooperation.

Generalized signal alignment for MIMO two-way X relay channels

We study the degrees of freedom (DoF) of MIMO two-way X relay channels. Previous work studied the case N <; 2M, where N and M denote the number of antennas at the relay and each source, respectively, and showed that the maximum DoF of 2N is achievable when N <; ⌊8M/5⌋ by applying signal alignment (SA) for network coding and interference cancelation. This work considers the case N > 2M where the performance is limited by the number of antennas at each source node and conventional SA is not feasible. We propose a generalized signal alignment (GSA) based transmission scheme. The key is to let the signals to be exchanged between every source node align in a transformed subspace, rather than the direct subspace, at the relay so as to form network-coded signals. This is realized by jointly designing the precoding matrices at all source nodes and the processing matrix at the relay. Moreover, the aligned subspaces are orthogonal to each other. By applying the GSA, we show that the DoF upper bound 4M is achievable when M ≤ ⌊ 2N/5 ⌋ (M is even) or M ≤ ⌊ 2N-1 /5 ⌋ (M is odd). Numerical results also demonstrate that our proposed transmission scheme is feasible and effective.

An efficient beamforming scheme for generalized MIMO two-way X relay channels

Recently, a multiple-input multiple-output (MIMO) two-way X relay channel, where two groups of source nodes each having 2 nodes exchange independent messages via a common relay node, was studied in [1]. In this paper, we extend it to the generalized MIMO two-way X relay channel, where m ≥ 2 and n ≥ 2 source nodes are contained in two groups, respectively. Based on signal space alignment, a new beamforming scheme is proposed to maximize the minimum effective signal to interference plus noise ratios (SINRs) among all data streams. The beamforming vectors are designed by an iterative algorithm in which a closed-form solution is obtained in each step. Moreover, we show that the power allocation problem given the shape of the beamformers can be transformed as a linear programming problem. Simulation results show that the proposed beamforming scheme can achieve significantly better error performance than random beamforming schemes subject to signal space alignment only.

Degrees of freedom of MIMO two-way X relay channel

In this paper, we study the degrees of freedom of a multiple-input multiple-output (MIMO) two-way X relay channel, i.e., a system with two groups of source nodes and one relay node, where each of the two source nodes in one group wants to exchange independent messages with both the two source nodes in the other group via the relay node. We only consider the symmetric case where each source node is equipped with M antennas while the relay is equipped with N antennas. We first show that the upper bound of the degrees of freedom is 2N when N ≤ 2M. Then by applying physical layer network coding and joint interference cancellation, we propose a novel transmission scheme for the considered network. We show that this scheme can always achieve this upper bound when N ≤ ⌊4M over 3⌋.

Coordinated beamforming design in multicell multicast networks

In this paper, we study the physical layer multicasting in multicell networks, where each base station equipped with multiple antennas transmits a common message using a single beamformer to multiple users equipped with a single antenna in the same cell. We consider the quality-of-service (QoS) beamforming for minimizing the total power consumption while guaranteeing that the signal-to-interference-plus-noise ratio (SINR) at each user is above a predetermined threshold. Based on the decomposition theory, we propose a novel decentralized algorithm to implement the coordinated beamforming with limited information sharing among different base stations. The algorithm is guaranteed to converge and in most cases it converges to the optimal solution. Simulation results demonstrate significant advantages of the proposed coordinated beamforming over conventional beamforming.